In the era of urbanization, a Multi-Modal Trip Planner (MMTP) is becoming essential to enabling optimized travel plans for the mobility needs of people. Increased traffic on roads affects commuting time and renders public transport schedules unpredictable. Hence, a travel plan generated by an MMTP based on static public transport schedules is inefficient and even obsolete. Besides, the rise of on-demand transportation services such as super shuttles, car-pooling, and shared rides provides better transportation options for commuters and adds more complexity to the MMTP, as these services do not have any schedules and are dynamically provided based on commuter request.
To handle the complexity and to provide better travel plans to the users, an MMTP needs to be “smart” enough to generate adaptive trip plans based on real-time dynamics such as the position of the commuters, public transports, on-road traffic, and on-demand transport services availability. Such a smart MMTP needs to track a commuter's geo position continuously to provide efficient adaptive travel plans and considering real-time dynamics. In the case of on-demand services, such tracking of commuters becomes more important so as to provide live information to co-commuters for better coordination among themselves and to enable additional features.
In addition, commuters willingly request MMTP to track their locations continuously and share it with friends and family for safety reasons or log it for simple travel journal bookkeeping. Modern smartphones can continuously track the commuter's location as he or she moves utilizing a GPS/GLONASS system and suffice the need of MMTP.
Such live tracking capability, however, implements a power hungry operation and can drain the battery of a smartphone or other portable computing device quite rapidly, which is a serious challenge.